Validation of machine learning angiography-derived physiological pattern of coronary artery disease.

Aims: The classification of physiological patterns of coronary artery disease (CAD) is crucial for clinical decision-making, significantly affecting the planning and success of percutaneous coronary interventions (PCIs). This study aimed to develop a novel index to reliably interpret and classify physiological CAD patterns based on virtual pullbacks from single-view Murray's law-based quantitative flow ratio (μFR) analysis.

Methods And Results: The pullback pressure gradient index (PPGi) was used to classify CAD patterns, with a cut-off value of PPGi = 0.78 distinguishing focal from diffuse and non-focal disease. The machine learning methods using penalized logistic regression and random forest were proposed to assess CAD patterns. Scores derived from multivariate functional principal component analysis of μFR and quantitative coronary analysis improved model performance. Expert panel interpretations served as the reference. A total of 343 vessels (291 patients) underwent classification. The PPGi cut-off of 0.78 achieved 67% accuracy [95% confidence interval (CI): 66-68%] for focal vs. diffuse and 76% accuracy (95% CI: 75-76%) for focal vs. non-focal classification. The penalized logistic regression model, including PPGi as a feature, provided superior accuracy: 88% (95% CI: 87-88%) for focal vs. diffuse and 81% (95% CI: 80-81%) for focal vs. non-focal classification. Moreover, the random forest model with PPGi as one of the features was applied for multiclass classification, providing an accuracy of 73% (95% CI: 73-73%).

Conclusion: The machine learning models for physiological patterns of CAD classification outperformed the binary PPGi method, providing robust and generalizable classification across different study populations.